Artificial Intelligence: Exploring Models and Mechanisms

What is Artificial Intelligence?

Artificial Intelligence (AI) is a broad category of software that has the ability to recognize patterns, learn from data, and produce useful outcomes. You have probably encountered AI in everyday situations. For example, when your mapping app reroutes you to avoid traffic, or when your bank flags a purchase as "unusual." Similarly, a customer support chatbot can answer common questions thanks to AI.

AI is a category rather than a single tool. Within this category are models, which are systems trained to learn from data and apply what they have learned to new situations. Some models specialize in areas such as speech, vision, or forecasting.

You are likely starting your journey in AI by using conversational AI tools, such as ChatGPT. The models behind ChatGPT specialize in language and are referred to as large language models.

Understanding How Large Language Models Work

A large language model (LLM) is a model designed to work with language. It learns patterns from vast amounts of text from numerous sources to generate and transform text in useful ways. An LLM does not "know" things like a person does. Instead, it predicts the most likely next piece of language based on context. Over time, advancements in computing power, training methods, and access to large datasets have enabled the construction of larger and more capable LLMs.

OpenAI and other leading research labs build these models as an essential part of their offerings, then make them available through user-facing products like ChatGPT or Codex, and via APIs, which allow developers to use these models to create their own AI tools and integrate AI into existing software.

How Models Evolve Over Time

New models become available in these research labs once they have been trained and have passed internal evaluations and safety tests. When you hear that an AI model has been "trained," it generally refers to two stages—think of it like someone learning and improving in their job.

The first stage is pre-training, when the model learns general patterns from a massive amount of text, giving it broad skills such as summarization, writing, translation, and explanation. Think of it as a new employee spending weeks reading everything they can—manuals, examples of excellent work, past projects, FAQs—until they understand the "shape" of the work.

Now, the "employee" begins to do the work, and a "manager" guides them: being clearer, asking good follow-up questions, adapting the appropriate tone, and following company policies. This is known as post-training. This stage helps the model follow instructions more reliably, communicate in a helpful style, and better handle delicate situations.

Post-training is also where safety checks are emphasized—a training designed to reduce harmful outputs, avoid unwanted requests, and respond more cautiously when the topic is sensitive or uncertain.

As models are updated and trained, you may notice changes in tone or responses. If you want consistent results, be explicit about your goal, audience, format, and constraints—and expect the model to be more cautious when safety or uncertainty is involved.

Reasoning Models and Non-Reasoning Models

Different models are tuned for different trade-offs—such as speed, depth, and how closely they follow multi-step instructions. Some are designed to respond quickly and fluidly to everyday tasks (writing, summarizing, rewriting, brainstorming). Others are designed to devote more computing power to thinking through a problem before responding, which can improve reliability on more complex and multi-step tasks.

Non-reasoning models (sometimes labeled as "Instant") are optimized for quick and fluid output. They are a good default choice when the task is simple and you primarily want momentum: transforming notes into a message, refining wording, generating options, or extracting key points.

Reasoning models (sometimes labeled as "Thinking") are trained to better solve problems deliberately and step-by-step—things like planning, complex analysis, delicate debugging, or decisions with constraints and edge cases. They may take longer, but they are often better at tracking multiple moving parts and avoiding superficial errors.

If you are just starting out, you don't need to worry about choosing the model—the default experience of ChatGPT is designed to change automatically so you can focus on your question, not the settings.

Over time, as you learn what you prefer (speed versus depth, quick drafts versus thorough analyses), you can start experimenting with optional controls: for example, choosing Auto most of the time, and switching to Thinking when the task is complex or high-stakes.

Summary

Here is the simple hierarchy:

• AI = the overall field
• Models = trained systems that perform specific tasks
• Large Language Models (LLMs) = models focused on understanding and generating language, trained over time by AI research labs
• ChatGPT = a product that helps you use an LLM effectively

Once you have this picture in mind, you will be ready to learn how to achieve excellent results with tools like ChatGPT—starting with how to talk to it to get the desired outcomes. Learn how to get started with ChatGPT and prompt engineering.